Project Summary IMOD is a freely available software package that provides tools for generating, viewing, and analyzing 3- dimensional (3-D) image data. Its main focus is on electron tomography, which involves the reconstruction of the interior of a specimen from a series of tilted views obtained with an electron microscope. The use of tomography has been growing rapidly in cell and structural biology research. Aside from being a tool for understanding the structures underlying biological functions, tomography can be used to obtain more health- related information, such as the structure of viruses or the changes in cells caused by particular mutations. The long-term goals of this project are: 1) to continue the development of the IMOD software so it will continue to offer a powerful and attractive package for tomographic and other 3-D reconstructions, even as methods and equipment evolve; and 2) to provide a resource that can be both used and built upon by others in the biological microscopy community. One aim is to expand the usefulness of automated tomographic reconstruction, which includes providing tools to support a pipeline for automatic reconstruction immediately after data acquisition. A second aim is to add new features to our two main programs with graphical user interfaces. This work will provide access to more operations in the user interface for tomographic reconstruction and other data processing. It will also add automated modeling of cellular membranes. This project will be a collaborative effort to automate the most time consuming step in tomographic analysis of cellular structure. A third aim is to provide more accurate reconstructions by implementing new algorithms for three different computations involved in generating a tomogram. A final aim is to improve and modernize some aspects of the underlying source code, to improve its performance, and to maintain the ability of the software to run on multiple operating systems. This work will enable IMOD to use the power of modern computers more efficiently and make it function better with the large data sets that are becoming increasingly common.